Title Page......Page 2
Copyright page......Page 5
Brief Contents......Page 6
Contents......Page 8
Preface......Page 18
About the Author......Page 24
Credits......Page 26
Learning Objectives......Page 28
What Is Business Analytics?......Page 31
Evolution of Business Analytics......Page 32
Impacts and Challenges......Page 35
Scope of Business Analytics......Page 36
Software Support......Page 39
Data for Business Analytics......Page 40
Data Sets and Databases......Page 41
Big Data......Page 42
Metrics and Data Classification......Page 43
Models in Business Analytics......Page 45
Decision Models......Page 48
Model Assumptions......Page 51
Prescriptive Decision Models......Page 53
Problem Solving with Analytics......Page 54
Structuring the Problem......Page 55
Implementing the Solution......Page 56
Key Terms......Page 57
Problems and Exercises......Page 58
Case: Drout Advertising Research Project......Page 60
Case: Performance Lawn Equipment......Page 61
Learning Objectives......Page 64
Basic Excel Skills......Page 66
Copying Formulas......Page 67
Other Useful Excel Tips......Page 68
Basic Excel Functions......Page 69
Functions for Specific Applications......Page 70
Insert Function......Page 71
Logical Functions......Page 72
Using Excel Lookup Functions for Database Queries......Page 74
Problems and Exercises......Page 77
Case: Performance Lawn Equipment......Page 79
Learning Objectives......Page 80
Data Visualization......Page 81
Tools and Software for Data Visualization......Page 82
Creating Charts in Microsoft Excel......Page 83
Column and Bar Charts......Page 84
Pie Charts......Page 86
Scatter Chart......Page 87
Bubble Charts......Page 89
Geographic Data......Page 90
Data Bars, Color Scales, and Icon Sets......Page 91
Sparklines......Page 92
Excel Camera Tool......Page 93
Data Queries: Tables, Sorting, and Filtering......Page 94
Pareto Analysis......Page 95
Filtering Data......Page 97
Statistical Methods for Summarizing Data......Page 99
Frequency Distributions for Categorical Data......Page 100
Relative Frequency Distributions......Page 101
Excel Histogram Tool......Page 102
Cumulative Relative Frequency Distributions......Page 106
Percentiles and Quartiles......Page 107
Cross-Tabulations......Page 109
Exploring Data Using PivotTables......Page 111
PivotCharts......Page 113
Slicers and PivotTable Dashboards......Page 114
Key Terms......Page 117
Problems and Exercises......Page 118
Case: Drout Advertising Research Project......Page 120
Case: Performance Lawn Equipment......Page 121
Learning Objectives......Page 122
Understanding Statistical Notation......Page 123
Arithmetic Mean......Page 124
Median......Page 125
Midrange......Page 126
Using Measures of Location in Business Decisions......Page 127
Interquartile Range......Page 128
Variance......Page 129
Standard Deviation......Page 130
Chebyshevβs Theorem and the Empirical Rule......Page 131
Standardized Values......Page 134
Coefficient of Variation......Page 135
Measures of Shape......Page 136
Excel Descriptive Statistics Tool......Page 137
Descriptive Statistics for Grouped Data......Page 139
Statistics in PivotTables......Page 141
Measures of Association......Page 142
Covariance......Page 143
Correlation......Page 144
Excel Correlation Tool......Page 146
Outliers......Page 147
Statistical Thinking in Business Decisions......Page 149
Variability in Samples......Page 150
Key Terms......Page 152
Problems and Exercises......Page 153
Case: Performance Lawn Equipment......Page 156
Learning Objectives......Page 158
Basic Concepts of Probability......Page 159
Probability Rules and Formulas......Page 161
Joint and Marginal Probability......Page 162
Conditional Probability......Page 164
Random Variables and Probability Distributions......Page 167
Discrete Probability Distributions......Page 169
Expected Value of a Discrete Random Variable......Page 170
Using Expected Value in Making Decisions......Page 171
Variance of a Discrete Random Variable......Page 173
Binomial Distribution......Page 174
Poisson Distribution......Page 176
Continuous Probability Distributions......Page 177
Properties of Probability Density Functions......Page 178
Uniform Distribution......Page 179
Normal Distribution......Page 181
Standard Normal Distribution......Page 183
Exponential Distribution......Page 185
Continuous Distributions......Page 187
Random Sampling from Probability Distributions......Page 188
Sampling from Discrete Probability Distributions......Page 189
Sampling from Common Probability Distributions......Page 190
Probability Distribution Functions in Analytic Solver Platform......Page 193
Data Modeling and Distribution Fitting......Page 195
Distribution Fitting with Analytic Solver Platform......Page 197
Key Terms......Page 199
Problems and Exercises......Page 200
Case: Performance Lawn Equipment......Page 206
Learning Objectives......Page 208
Sampling Methods......Page 209
Estimating Population Parameters......Page 212
Errors in Point Estimation......Page 213
Understanding Sampling Error......Page 214
Sampling Distribution of the Mean......Page 216
Interval Estimates......Page 217
Confidence Intervals......Page 218
Confidence Interval for the Mean with Known Population Standard Deviation......Page 219
The t-Distribution......Page 220
Confidence Interval for a Proportion......Page 221
Using Confidence Intervals for Decision Making......Page 223
Prediction Intervals......Page 224
Confidence Intervals and Sample Size......Page 225
Problems and Exercises......Page 227
Case: Drout Advertising Research Project......Page 229
Case: Performance Lawn Equipment......Page 230
Learning Objectives......Page 232
Hypothesis Testing......Page 233
One-Sample Hypothesis Tests......Page 234
Understanding Potential Errors in Hypothesis Testing......Page 235
Selecting the Test Statistic......Page 236
Drawing a Conclusion......Page 237
p-Values......Page 239
One-Sample Tests for Proportions......Page 240
Confidence Intervals and Hypothesis Tests......Page 241
Two-Sample Tests for Differences in Means......Page 242
Two-Sample Test for Means with Paired Samples......Page 245
Test for Equality of Variances......Page 246
Analysis of Variance (ANOVA)......Page 248
Assumptions of ANOVA......Page 250
Chi-Square Test for Independence......Page 251
Cautions in Using the Chi-Square Test......Page 253
Key Terms......Page 254
Problems and Exercises......Page 255
Case: Performance Lawn Equipment......Page 258
Learning Objectives......Page 260
Modeling Relationships and Trends in Data......Page 261
Simple Linear Regression......Page 265
Finding the Best-Fitting Regression Line......Page 266
Least-Squares Regression......Page 268
Simple Linear Regression with Excel......Page 270
Testing Hypotheses for Regression Coefficients......Page 272
Residual Analysis and Regression Assumptions......Page 273
Checking Assumptions......Page 275
Multiple Linear Regression......Page 276
Building Good Regression Models......Page 281
Correlation and Multicollinearity......Page 283
Practical Issues in Trendline and Regression Modeling......Page 284
Regression with Categorical Independent Variables......Page 285
Categorical Variables with More Than Two Levels......Page 288
Regression Models with Nonlinear Terms......Page 290
Advanced Techniques for Regression Modeling using XLMiner......Page 292
Problems and Exercises......Page 295
Case: Performance Lawn Equipment......Page 299
Learning Objectives......Page 300
Historical Analogy......Page 301
Indicators and Indexes......Page 302
Statistical Forecasting Models......Page 303
Moving Average Models......Page 305
Error Metrics and Forecast Accuracy......Page 309
Exponential Smoothing Models......Page 311
Forecasting Models for Time Series with a Linear Trend......Page 313
Double Exponential Smoothing......Page 314
Regression-Based Forecasting for Time Series with a Linear Trend......Page 315
Regression-Based Seasonal Forecasting Models......Page 317
Holt-Winters Models for Forecasting Time Series with Seasonality and Trend......Page 319
Selecting Appropriate Time-Series-Based Forecasting Models......Page 321
Regression Forecasting with Causal Variables......Page 322
The Practice of Forecasting......Page 323
Problems and Exercises......Page 325
Case: Performance Lawn Equipment......Page 327
Learning Objectives......Page 328
The Scope of Data Mining......Page 330
Sampling......Page 331
Data Visualization......Page 333
Dirty Data......Page 335
Cluster Analysis......Page 337
Classification......Page 342
Measuring Classification Performance......Page 343
Using Training and Validation Data......Page 345
Classification Techniques......Page 347
k-Nearest Neighbors (k-NN)......Page 348
Discriminant Analysis......Page 350
Logistic Regression......Page 355
Association Rule Mining......Page 359
Cause-and-Effect Modeling......Page 362
Problems and Exercises......Page 365
Case: Performance Lawn Equipment......Page 367
Learning Objectives......Page 368
Building Models Using Simple Mathematics......Page 369
Building Models Using Influence Diagrams......Page 370
Spreadsheet Design......Page 371
Spreadsheet Quality......Page 373
Spreadsheet Applications in Business Analytics......Page 376
Models Involving Multiple Time Periods......Page 378
Single-Period Purchase Decisions......Page 380
Overbooking Decisions......Page 381
Data and Models......Page 383
Range Names......Page 386
Form Controls......Page 387
What-If Analysis......Page 389
Data Tables......Page 391
Scenario Manager......Page 393
Goal Seek......Page 394
Parametric Sensitivity Analysis......Page 395
Tornado Charts......Page 397
Problems and Exercises......Page 398
Case: Performance Lawn Equipment......Page 403
Learning Objectives......Page 404
Monte Carlo Simulation......Page 406
Defining Uncertain Model Inputs......Page 408
Running a Simulation......Page 411
Viewing and Analyzing Results......Page 413
New-Product Development Model......Page 415
Confidence Interval for the Mean......Page 418
Overlay Charts......Page 419
Box-Whisker Charts......Page 421
The Flaw of Averages......Page 422
Monte Carlo Simulation Using Historical Data......Page 423
Monte Carlo Simulation Using a Fitted Distribution......Page 424
Overbooking Model......Page 425
The Custom Distribution in Analytic Solver Platform......Page 426
Cash Budget Model......Page 427
Correlating Uncertain Variables......Page 430
Problems and Exercises......Page 434
Case: Performance Lawn Equipment......Page 441
Learning Objectives......Page 442
Identifying Elements for an Optimization Model......Page 443
Translating Model Information into Mathematical Expressions......Page 444
More about Constraints......Page 446
Implementing Linear Optimization Models on Spreadsheets......Page 447
Solving Linear Optimization Models......Page 449
Using the Standard Solver......Page 450
Using Premium Solver......Page 452
Solver Answer Report......Page 453
Graphical Interpretation of Linear Optimization......Page 455
How Solver Works......Page 460
Solver Outcomes and Solution Messages......Page 462
Alternative (Multiple) Optimal Solutions......Page 463
Unbounded Solution......Page 464
Infeasibility......Page 465
Using Optimization Models for Prediction and Insight......Page 466
Solver Sensitivity Report......Page 468
Using the Sensitivity Report......Page 471
Parameter Analysis in Analytic Solver Platform......Page 473
Problems and Exercises......Page 477
Case: Performance Lawn Equipment......Page 482
Learning Objectives......Page 484
Types of Constraints in Optimization Models......Page 486
Process Selection Models......Page 487
Spreadsheet Design and Solver Reports......Page 488
Solver Output and Data Visualization......Page 490
Blending Models......Page 494
Dealing with Infeasibility......Page 495
Portfolio Investment Models......Page 498
Evaluating Risk versus Reward......Page 500
Scaling Issues in Using Solver......Page 501
Transportation Models......Page 503
Formatting the Sensitivity Report......Page 505
Multiperiod Production Planning Models......Page 507
Building Alternative Models......Page 509
Multiperiod Financial Planning Models......Page 512
Models with Bounded Variables......Page 516
Auxiliary Variables for Bound Constraints......Page 520
A Production/Marketing Allocation Model......Page 522
Using Sensitivity Information Correctly......Page 524
Problems and Exercises......Page 526
Case: Performance Lawn Equipment......Page 538
Learning Objectives......Page 540
Solving Models with General Integer Variables......Page 541
Workforce-Scheduling Models......Page 545
Alternative Optimal Solutions......Page 546
Integer Optimization Models with Binary Variables......Page 550
Project-Selection Models......Page 551
Using Binary Variables to Model Logical Constraints......Page 553
Location Models......Page 554
Parameter Analysis......Page 556
A Customer-Assignment Model for Supply Chain Optimization......Page 557
Plant Location and Distribution Models......Page 560
Binary Variables, IF Functions, and Nonlinearities in Model Formulation......Page 561
Fixed-Cost Models......Page 563
Problems and Exercises......Page 565
Case: Performance Lawn Equipment......Page 574
Learning Objectives......Page 580
Formulating Decision Problems......Page 582
Decision Strategies for a Minimize Objective......Page 583
Decision Strategies for a Maximize Objective......Page 584
Decisions with Conflicting Objectives......Page 585
Expected Value Strategy......Page 587
Evaluating Risk......Page 588
Decision Trees......Page 589
Decision Trees and Risk......Page 593
Sensitivity Analysis in Decision Trees......Page 595
The Value of Information......Page 596
Bayesβs Rule......Page 597
Utility and Decision Making......Page 599
Constructing a Utility Function......Page 600
Exponential Utility Functions......Page 603
Problems and Exercises......Page 605
Case: Performance Lawn Equipment......Page 609
Appendix A......Page 612
Glossary......Page 636
Index......Page 644